Limit theory for M-estimation for infinite variance processes.
Published: 2004
Language: eng
Number of pages: 158
Description: It is well known that the Gaussian assumption on innovations of time series models leads to a strong limitation on the applicability of those models, especially in modeling financial and econometric variables, mainly because of the asymmetry and heavy-tailedness of the distributions of innovations. In this study we develop asymptotic theory for M-estimators under the assumption of infinite variance innovations. We consider the estimation of parameters in three models, namely autoregressive process with a unit root, vector autoregressions with integrated processes and econometric cointegration process, and derive the limiting distributions of M-estimators. We assume that the innovations are in the domain of attraction of a stable law.
Notes: Adviser: Keith Knight.

Thesis (Ph.D.)--University of Toronto, 2004.

Electronic version licensed for access by U. of T. users.

Source: Dissertation Abstracts International, Volume: 65-05, Section: B, page: 2471.